Socioeconomic Status, Lifestyle, and DNA Methylation Age Among Racially and Ethnically Diverse Adults

This cohort study explores whether the rate of biological aging estimated by an epigenetic clock is associated with social determinants of health in a racially and ethnically diverse population.


Introduction
2][3] This emerging field posits that racism or social disenfranchisement (proxied via race and ethnicity) shapes personal experiences that affect gene function over the lifespan through epigenetic mechanisms, including small noncoding RNAs, histone modifications, and DNA methylation.5][6] Given technological advances to examine epigenetic variability in population-based studies, reports abound documenting discordance between chronological and biological age, with the latter having a stronger correlation with health. 7However, how such differences relate to racial and ethnic health disparities, which remain pervasive in the US, has not been fully elucidated.
Biological aging, which has been associated with morbidity, mortality, health behaviors, and social experiences, [7][8][9][10] can be estimated from lymphocyte DNA methylation levels at specific genomic loci.Known as epigenetic clocks, these estimates were developed through machine learning on microarray-based DNA methylation data.First-generation Horvath 7,11 and Hannum 12 clocks accurately predicted age by cross-sectionally analyzing specific DNA methylation loci across various ages, 12,13 while second-generation clocks, such as PhenoAge, improved predictions related to biological aging and disease by additionally being trained on biomarker data. 14The latest, thirdgeneration clock, Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), uniquely measures the rate of aging, having been trained on longitudinal change in biomarkers with DNA methylation data. 15,166][17] However, to our knowledge, this newer clock has yet to be fully examined in racially and ethnically diverse populations typically underrepresented in biomedical research, including those that experience significant environmental, social, and economic inequities that may affect biological aging and underlie health disparities.
9][20][21][22][23] In Hawaii, Native Hawaiian residents have higher rates of diabetes, 24,25 obesity, 26 and cardiovascular disease 27,28 than White residents; those living in low NSES areas have the highest mortality rates, particularly from heart disease. 18Meanwhile, Japanese American residents in Hawaii have higher rates of diabetes than White residents 29 and have the lowest overall mortality in high NSES areas 18 ; those in low NSES areas are more likely to have obesity and diabetes than those in higher NSES areas. 30Importantly, these cardiometabolic health disparities appear at a significantly younger age among Japanese American and Native Hawaiian residents than among White residents and are associated with adverse sociobehavioral factors, 31 implicating differences in the rate of biological aging.Prior research has reported associations of SDOHs with biological aging as measured by older-generation clocks, 14,32 while recent studies have only begun to explore racial and ethnic  33,34 In this study, we examined the association of NSES and sociobehavioral factors with biological aging measured by DunedinPACE, the only epigenetic clock to estimate the rate of aging, in a cross-sectional analysis of Hawaii's multiethnic and understudied population.

Study Population
Described in detail elsewhere, 35

Data Collection
At cohort entry (1993-1996), participant data were collected using a detailed 26-page mailed questionnaire on various topics, including demographics, behaviors such as physical activity (PA), and educational level ranging from sixth grade to postgraduate education.Race and ethnicity were self-reported using a standardized survey instrument, and diet quality was measured using the Healthy Eating Index (HEI) 2010, derived from a self-administered food frequency questionnaire. 36e HEI score is a measure for adherence to the Dietary Guidelines for Americans; scores range from 0 to 100, with higher scores indicating better alignment with key dietary recommendations that support health.Where indicated, stratification into high and low HEI diet quality groups was based on previous studies. 37At blood sample collection (2004-2005), body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was determined from self-reported height and weight and separated into categories as defined elsewhere 38,39 ; participant age was recorded at that time and used in analyses.Moderate or vigorous PA was assessed using categories of high (>16 h/wk) and low (Յ16 h/wk) groups as in another study. 40The US census data from 1990 (the census closest to evaluation of exposures at baseline and used in subsequent data analysis) were linked to participants' addresses and analyzed with principal component analysis to create an NSES index comprising educational level, occupation, employment status, household income, poverty, rent, and house value data, as detailed elsewhere. 30The NSES index was categorized into quintiles for all Hawaii MEC participants and further defined as low for quintiles 1 to 3 and high for quintiles 4 to 5.
Molecular and cell phenotyping data from cryopreserved lymphocytes were generated from November 2017 to June 2021, with data analysis taking place from January 2022 to May 2024.

Monocyte Isolation and Validation by Flow Cytometry
From each participant, 10 mL of blood was obtained by venipuncture into an acid citrate dextrose tube.Peripheral blood mononuclear cells were isolated using an ACCUSPIN tube (Sigma-Aldrich), cryopreserved, and stored in liquid-phase nitrogen until analysis.The samples were later thawed and used to isolate monocytes using the EasySep Negative Selection, Human Monocyte Enrichment Kit without CD16 depletion and the EasyEights EasySep magnet cell separator (STEMCELL Technologies). 41Monocyte purity was verified from 50 000 cell aliquots per sample using flow cytometry as described previously. 42,43An enrichment success threshold was set at more than 65% monocytes to limit DNA methylation variability caused by cell-type heterogeneity. 43

JAMA Network Open | Genetics and Genomics
Socioeconomic Status, Lifestyle, and DNA Methylation Age

DNA Methylation Quantification
Nucleic acids from enriched monocytes were extracted using the AllPrep DNA/RNA Mini Kit (Qiagen).
DNA samples were bisulfite-converted and hybridized to the Infinium MethylationEPIC BeadChip microarray (Illumina, Inc) as previously described. 42Data processing involved R, version 4.1.2(R Project for Statistical Computing) with the minfi, version 1.40 framework. 44Samples and probes returning a mean detection P Ն 0.01 were omitted.ENmix, version 1.30.03(Bioconductor) was used to normalize microarray data using the out-of-bounds method, and dye bias was corrected using RELIC. 45Sex chromosomes were removed, and single-nucleotide variants and cross-reactive probes were eliminated using maxprobes, version 0.02, an open-source code in R. 46 Probe-associated bias was controlled and monocyte enrichment was corroborated by comparing DNA methylation data of each sample with known cell-sorted, monocyte-specific DNA methylation states 47 as previously described. 48,49DunedinPACE scores were calculated from β matrices using the DunedinPACE package, version 0.99. 50

Statistical Analysis
Means were compared between groups using analysis of variance, and percentages were compared using χ 2 tests.To account for multiple comparisons when contrasting the differences in means by sociobehavioral variables across race and ethnicity groups, adjusted P values were calculated by applying the Bonferroni method (2-sided P < .10 was considered significant).Linear regression models of DunedinPACE scores (where a score of 1.0 means equivalent biological and chronological aging) were used to examine associations with age (linear and quadratic terms), race and ethnicity, sex (female, male), and the sociobehavioral variables of NSES (low, high), BMI, educational level, diet via HEI, and PA as total hours of combined moderate and vigorous physical activities per week.All independent variables were entered continuously unless otherwise specified.Initially, a main effect model was fit.Next, the interactions between race and ethnicity and each of the sociobehavorial variables were evaluated in separate models.Models with significant interactions are presented.
Significance was assessed by the Wald statistic.Covariate-adjusted means were computed by subgroup at the mean vector of other independent variables.A sensitivity analysis was performed in which educational level was parameterized categorically as less than college, college degree, and advanced degree.The intraclass correlation within census tracts for the NSES variable was not accounted for as there were too few individuals per tract for covariance estimation.and DunedinPACE scores exhibited a weak negative correlation overall (R = −0.09;P = .08),and no correlation was found among the Japanese American, Native Hawaiian, or White groups separately.

Sample Characteristics and Associations With DunedinPACE Estimates of Biological Aging
However, consistent with their opposite correlations with DunedinPACE scores, BMI and NSES exhibited a significant negative correlation overall (R = −0.14;P = .008)(Figures 1B and 2B).
Educational attainment varied by race and ethnicity.Native Hawaiian participants exhibited a lower mean (SE) number of years of education at 14.07 (0.19) compared with White participants at 15.47 (0.22) (adjusted P < .001).Among participants overall, those with a low level of education (less than 12th grade) exhibited a significantly higher mean (SE) DunedinPACE score than those with a higher level (more than 12th grade) of education (1.29 [0.01] vs 1.25 [0.01]; P = .005);yet, this difference was statistically significant only among Japanese American participants (eFigure 4 in Supplement 1).Additionally, the level of education and the DunedinPACE score among participants overall were significantly negatively correlated (R = −0.15;P = .003);there was a weak negative correlation among Native Hawaiian participants (R = −0.15;P = .07)and a strong negative correlation

Discussion
In this sample of MEC study participants, the mean DunedinPACE score was 1.27, indicating a 27% faster aging rate than that of the original Dunedin study, 15 where a score of 1.0 means equivalent biological and chronological aging.This aging rate varied by sex, with females and males aging 28% and 25% faster, respectively, than expected.Like the original study 15 and a recent one to examine DunedinPACE in African American adults, 33 we found no differences in DunedinPACE scores between female and male White participants.In contrast, previous research using older epigenetic clocks noted sex differences in aging rates among Hispanic and White adults. 51,52Similarly, we

Figure 1 .R
Figure 1.Overall Associations of Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE) Score With Sociobehavioral Factors

Figure 2 .
Figure 2. Associations Between Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE) Score and Sociobehavioral Factors by Race and Ethnicity
the Multiethnic Cohort (MEC) study was established from May 1993 to September 1996 to understand the association of race and ethnicity with cancer and chronic disease rates.This cohort study used a cross-sectional analysis of data from included healthy Hawaii residents selected to prospectively examine diabetes development who self-reported as Japanese 15nedinPACE score among these participants was significantly higher at 1.31 (0.01) than among White participants at 1.22 (0.01) and Japanese American participants at 1.25 (0.01), indicating substantial accelerated biological aging in Native Hawaiian participants.The DunedinPACE classification also differed by race and ethnicity; 161 of the overall participants (42.8%) exhibited fast DunedinPACE, defined as a DunedinPACE score of 1.29 or higher, as in other studies.15TheNativeHawaiiangroup had the highest proportion with fast DunedinPACE at 81 participants (56.3%) followed by the Japanese American group at 42 (37.2%)and the White group at 38 (31.9%).Overall, DunedinPACE and chronological age were not correlated; yet, similarly to a previous study of White adults,15among Japanese American participants, a strong positive correlation was found between DunedinPACE and chronological age (eFigure 3 in Supplement 1).The mean (SE) DunedinPACE score Table.Summary Statistics of Study Participant Data significantly higher among females compared with males overall (1.28 [0.01] vs 1.25 [0.01]; P = .005),with a significant difference between females and males observed among Native Hawaiian and Japanese American participants but not among White participants (eFigure 3 in Supplement 1).The mean DunedinPACE scores were compared by subgroup, overall, and by race and ethnicity with Bonferroni adjustment of P values.Body mass index significantly varied by race and ethnicity.Based on Hawaii NSES parameters of the MEC study established previously, 18 the distribution of participants living in low and high NSES areas differed by race and ethnicity.Native Hawaiian participants had the lowest proportion living in high NSES areas at 71 (49.3%) compared with 81 (68.0%) for White participants and 72 (63.7%) for Japanese American participants.Overall, overall mean (SE) age of 57.81 (0.38) years; age significantly varied between race and ethnicity categories.Native Hawaiian participants exhibited the youngest mean (SE) age at 55.60 (0.54) years followed by White participants at 58.22 (0.64) years and Japanese American participants at 60.19 (0.78) years.The mean (SE) DunedinPACE score for the overall population was 1.27 (0.01).Despite Native Hawaiian participants having the lowest chronological mean age in the study, the mean (SE) a Data are presented as number (percentage) of participants unless otherwise indicated.bP values were from a Pearson χ c A score of 1.0 means equivalent biological and chronological aging.dScore range, 0 to 100, with higher scores indicating better alignment with key dietary recommendations that support health.was individuals living in low NSES areas exhibited significantly higher mean (SE) DunedinPACE scores compared with those in high NSES areas (1.28 [0.01] vs 1.25 [0.01]; P = .03);yet, this difference was not statistically significant in each race and ethnicity category (eFigure 4 in Supplement 1).Also, NSES score was 1.26 (0.86) for Japanese American, 1.32 (0.80) for Native Hawaiian, and 1.20 (0.82) for White participants at 12 years of education and 1.24 (0.86) for Japanese American, 1.31 (0.80) for Native Hawaiian, and 1.23 (0.82) for White participants at 16 years of education.The level of education, treated as a categorical variable, was also used in a sensitivity model.On average, those with advanced degrees among Japanese American participants had significantly lower DunedinPACE scores than those with college degrees (β, −0.072; 95% CI, −0.142 to −0.001; a P < .001.b P < .10. c P < .05.DunedinPACE Interaction of educational level with race and ethnicity Cohort Participants Residing in Areas of Diverse NSES eFigure 2. Flow Chart of Inclusion and Exclusion Criteria of Samples From Cohort Participants eFigure 3. Associations Between Age, DunedinPACE, Race and Ethnicity, and Sex eFigure 4. Distribution of DunedinPACE by Sociobehavioral Factors eFigure 5. Linear Regression Analyses of DunedinPACE and Sociobehavioral Factors With Educational Level as Categories eTable.Data of Linear Regression Analyses of DunedinPACE and Sociobehavioral Factors AInteraction of physical activity with race and ethnicity